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(Ebook) Deep Learning with JavaScript: Neural networks in TensorFlow.js by Shanqing Cai; Stanley Bileschi; Eric D. Nielsen ISBN 9781617296178, 1617296171

  • SKU: EBN-27166480
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Instant download (eBook) Deep Learning with JavaScript: Neural networks in TensorFlow.js after payment.
Authors:Shanqing Cai; Stanley Bileschi; Eric D. Nielsen
Pages:560 pages.
Year:2020
Editon:1
Publisher:Manning Publications
Language:english
File Size:9.87 MB
Format:mobi
ISBNS:9781617296178, 1617296171
Categories: Ebooks

Product desciption

(Ebook) Deep Learning with JavaScript: Neural networks in TensorFlow.js by Shanqing Cai; Stanley Bileschi; Eric D. Nielsen ISBN 9781617296178, 1617296171

Deep learning has transformed the fields of computer vision, image processing, and natural language applications. Thanks to TensorFlow.js, now JavaScript developers can build deep learning apps without relying on Python or R. Deep Learning with JavaScript shows developers how they can bring DL technology to the web. Written by the main authors of the TensorFlow library, this new book provides fascinating use cases and in-depth instruction for deep learning apps in JavaScript in your browser or on Node.about the technologyRunning deep learning applications in the browser or on Node-based backends opens up exciting possibilities for smart web applications. With the TensorFlow.js library, you build and train deep learning models with JavaScript. Offering uncompromising production-quality scalability, modularity, and responsiveness, TensorFlow.js really shines for its portability. Its models run anywhere JavaScript runs, pushing ML farther up the application stack.about the bookIn Deep Learning with JavaScript, you’ll learn to use TensorFlow.js to build deep learning models that run directly in the browser. This fast-paced book, written by Google engineers, is practical, engaging, and easy to follow. Through diverse examples featuring text analysis, speech processing, image recognition, and self-learning game AI, you’ll master all the basics of deep learning and explore advanced concepts, like retraining existing models for transfer learning and image generation.
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